Streamlined, guided on-couch adaptive workflow

ABSTRACT

Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

FIELD

The present disclosure relates generally to adaptive radiation therapy,and more particularly, to systems, methods, and devices for generating astreamlined, guided on-couch adaptive workflow for building a sessionpatient model and selecting a treatment option in time sensitivetreatment sessions.

BACKGROUND

Radiation therapy involves medical procedures that use externalradiation beams to treat pathological anatomies (tumors, lesions,vascular malformations, nerve disorders, etc.) by delivering prescribeddoses of radiation (X-rays, gamma rays, electrons, protons, and/or ions)to the pathological anatomy, while minimizing radiation exposure to thesurrounding tissue and critical anatomical structures.

In general, radiation therapy treatments consist of several phases.First, a precise three-dimensional (3D) map of the anatomical structuresin the area of interest (head, body, etc.) is constructed using any oneof (or combinations thereof) a computed tomography (CT), cone-beam CBCT,magnetic resonance imaging (MRI), positron emission tomography (PET), 3Drotational angiography (3DRA), or ultrasound techniques. This determinesthe exact coordinates of the target within the anatomical structure,namely, locates the tumor or abnormality within the body and defines itsexact shape and size. Second, a motion path for the radiation beam iscomputed to deliver a dose distribution that the radiation oncologistfinds acceptable, considering a variety of medical constraints. Duringthis phase, a team of specialists develop a treatment plan using specialcomputer software to optimally irradiate the tumor and minimize dose tothe surrounding normal tissue by designing beams of radiation toconverge on the target area from different angles and planes. Third, theradiation treatment plan is executed. During this phase, the radiationdose is delivered to the patient according to the prescribed treatmentplan. Generally, a treatment plan is delivered to the patient over aseries of radiation treatments referred to as fractions.

There are many factors that can contribute to differences between theprescribed radiation dose distribution and the actual dose delivered(i.e., the actual dose delivered to the target during the radiationtreatment). One such factor is uncertainty in the patient's position inthe radiation therapy system. Other factors involve uncertainty that isintroduced by changes that can occur during the patient's treatment.Such changes can include random errors, such as small differences in apatient's setup position. Other sources are attributable tophysiological changes that might occur if a patient's tumor regresses orif the patient loses weight during therapy. Another category ofuncertainty includes motion. Motion can potentially overlap with eitherof the categories as some motion might be more random and unpredictable,whereas other motion can be more regular.

These anatomical and physiological changes can cause the target volumesand surrounding anatomical structures and organs to move and change insize and shape during the therapy. As such, continuing to follow theinitial treatment plan may result in an actual received dosedistribution that differs from the planned distribution, and thusreduced doses to target volumes and/or increased doses to organs at risk(OARs). Adapting the treatment plan, namely, making modifications to theinitial treatment plan to match the new location and shape of the targetvolume and surrounding anatomical structures based on subsequentlyacquired image data is one way to rectify this issue.

Adaptive radiation therapy is a process by which, using subsequentimages, an original treatment plan can be adjusted to counteract theseanatomical changes. The adaptive radiation therapy process is aclosed-loop radiation treatment process where the treatment plan can bemodified using a systematic feedback of measurements. By systematicallymonitoring treatment variations and by incorporating them to re-optimizethe treatment plan during the course of treatment, the adaptiveradiation therapy improves radiation treatment.

Adaptive radiation therapy can occur at three different timescales,namely, off-line between treatment fractions, on-line immediately priorto a treatment fraction, and in real-time during a treatment fraction.

In an off-line adaptive therapy process, during each treatment fraction,a new image (CT or CTBC image, for example) of the patient is acquiredbefore or after each of the fractions and the images are evaluated todetermine multi-day locations of the target volumes. Based on this, anew plan can be developed to better reflect the range of motion of thetarget volumes. Off-line adaptive processes are suitable for treatinghead and neck tumors, for example, where the changes are generallygradual, predictable, and slow.

In an on-line adaptive therapy process, the radiation therapy system canbe used prior to a fraction to validate or adjust the patient treatmentplan for the treatment delivery. The imaging system can thus be used toconcurrently modify the treatment delivery to reflect the changes in thepatient's anatomy. On-line adaptive processes are suitable for treatingtumors located in the pelvic and upper abdominal regions where organmotions are stochastic, and changes are large, fast occurring andunpredictable, or for treating tumors in thoracic region where thechanges are generally abrupt and persistent.

In a real-time (on-couch) adaptive therapy process, the radiationtherapy system can be used during a treatment fraction. On-couchadaptive radiation therapy allows adjustment of treatment plan based ontumor and anatomical changes while the patient is on the treatmenttable. On-couch adaptive processes are suitable for treating lung tumorsfor example, where the changes are abrupt and persistent as well aspelvis/abdominal tumors where the changes are generally large andunpredictable. Its application, however, is limited to most needingpatients due to time, workload burden and expertise required to performon-couch adaptive radiation therapy.

Adaptive radiation therapy can also allow for recalculating thedelivered dose after each fraction and accumulate these doses utilizingimage deformation techniques during the accumulation to account forinternal motions. The calculated doses can then be compared to theplanned dose, and if any discrepancies are noted, subsequent fractionscan be modified to account for the changes.

As the impact of anatomical changes and/or patient positioning dependson the individual patient and the details of the treatment plan, nogeneral rules are applied to indicate when re-imaging and re-planning isto be done. Instead, in-treatment imaging is reviewed one or multipletimes during a treatment regimen by medical personnel to verifyreproducibility of patient positioning and assess anatomical changes todetermine if re-planning is necessary. Subsequently, the initialtreatment plan is copied onto the new image, and the dosimetric changesare assessed. If these dosimetric changes show that the target coverageor that OAR sparing may be compromised, the relevant anatomicalstructures are re-contoured, and a new treatment plan is created.

An exemplary workflow for adaptive radiation therapy is shown in FIG.18. As shown in FIG. 18, after the initial set-up of the patient in theradiation treatment device (Patient Setup), a treatment session image istaken of the patient for plan adaptation (Imaging), followed by thegenerating of a patient session model that includes the contouring ofthe OARs and the target structure of interest (i.e., target volume) onthe treatment session image. The contouring (segmentation, delineation)can be done manually or automatically. Manual segmentation, however, isnot only time consuming, but also requires specialized knowledge as wellas an expert (i.e., physician) to be present to do it. As such, usingmanual segmentation is not beneficial for real-time (on-couch) adaptivetherapy.

On the other hand, automatically generated contours are prone to errorsand uncertainties because both the target volumes and OAR structures aresegmented in one step using a general-purpose algorithm. As such, thesecontours need to be further reviewed and edited by an expert, which addsnot only additional time to the workflow but also requires the presenceof an expert. Moreover, since the revision of the generated contoursdoes not eliminate the errors that were present in the deformation(i.e., in the deformation vector field DVF), these errors will propagateto the electron density map calculated using the deformation vectorfield (DVF), which also adds time and need for an expert to manuallycorrect the errors in the density map.

Furthermore, in the current planning selection process (Planning andEvaluation) it is the expert who evaluates and chooses the proper plannormalization parameters and makes plan parameter modifications which isselected on per case basis, and thus not only that it requires that anexpert be present, but also increases the risk that the wrong criteriawill be selected. Moreover, in current workflows, the final plan isfurther evaluated and approved by the expert before it can be appliedfor treatment delivery, which again increases the time and expertiserequired. In current adaptive workflows there is also a time andcomputation penalty if the scheduled plan is not selected.

Thus, in current adaptive workflows, on-line adaptive planning takes along time due to the fact that at several steps in the adaptive planningprocess an expert (i.e., physician) needs to be present to generate,evaluate, edit, and correct the results at each step involved in there-planning process, since many of the currently applied processes areeither not automated or they include uncertainties that either give riseto errors or propagate existing errors.

There is thus a need for an adaptive planning workflow that allows foradapting a treatment plan to the anatomy of the patient at everyfraction while the patient is on the treatment couch that is fast,accurate, and limits the need for an expert/physician to be presentduring the treatment re-planning.

SUMMARY

Embodiments of the disclosed subject matter enable on-couch adaptivetherapy workflows which minimize time needed to confidently create asession patient model, select an appropriate treatment plan for thetreatment session, and treat the patient.

Embodiments of the disclosed subject matter enable creation of aworkflow that is guided by prescribed adaptive directives provided bythe departmental information system.

Embodiments of the disclosed subject matter enable the creation of aworkflow where the quantitative criteria for plan adaptation areselected by the prescriber and automatically provided by thedepartmental information system to the on-couch adaptive system.

Embodiments of the disclosed workflow integrates plan checks whichminimizes time spent on ensuring safety of the selected plan.

In disclosed embodiments, the treatment session patient model is builtin a step wise fashion, starting from the most variable anatomy. Thisallows to build user confidence and provide a better starting point forsubsequent segmentation algorithms, increasing chances of successfulautomatic generation of other structures.

In disclosed embodiments, an automated workflow for an adaptive therapysession is provided, comprising: obtaining a set of directives, the setof directives including data representing a planned treatment for apatient; and using the set of directives to perform a series ofautomated steps to: generate a session patient model in a step-wisefashion; generate a first and a second treatment plan for the sessionpatient model; and select a treatment plan that is appropriate for acurrent treatment session.

In exemplary embodiments, the generating of the session patient model ina step-wise fashion includes: a first step wherein a treatment sessionimage of a portion of the patient is generated, the treatment sessionimage containing an anatomy of interest and optionally a bone structureof interest; an optional second step wherein the treatment session imageis evaluated using a reference image from the set of directives, thereference image containing a corresponding reference bone structure, thetreatment session image being accepted when the bone structure ofinterest in the treatment session image matches the bone structure inthe reference image; a third step wherein, upon acceptance of thetreatment session image, one or more influencer structures from the listof influencer structures are generated on the treatment session image; afourth step wherein the generated influencer structures are evaluatedbased on one or more directives of the set of directives; a fifth stepwherein, upon acceptance of the generated influencer structures, one ormore target volumes from the reference patient model are propagated tothe treatment session image; and a sixth step wherein the propagated oneor more target volumes are evaluated based on one or more directives ofthe set of directives. Upon acceptance of the propagated target volumes,the treatment session image including the influencer structures and thepropagated target volumes is accepted as the session patient model.Alternatively, the second step is performed after the target volumeevaluation step.

In disclosed embodiments, the treatment isocenter can be automaticallydetermined and, using the control points of the reference plan which canbe automatically transferred and the treatment session patient model, aso-called Scheduled Plan can be generated. Using the knowledge acquiredduring the initial planning (provided by the departmental informationsystem, for example), a new plan, namely, an Adapted Plan, can beautomatically optimized. As result, the user is presented with two plansfor a current treatment session: Scheduled Plan and Adapted Plan.

In disclosed embodiments, the plans are presented in a comparison viewallowing the user to always chose the plan appropriate for the sessionanatomy without the need for additional computation and usermanipulation of patient data.

In disclosed embodiments, a method for selecting, by a user, a treatmentplan to be applied in a treatment session in an automated adaptiveradiotherapy is described, the method comprising the following steps:generating a treatment session image; generating a target volume in thetreatment session image; determining, by the user, whether to accept thegenerated target volume; generating a first treatment plan based ontreatment isocenter information obtained by automatically aligning ofthe accepted generated target volume in the treatment session image witha corresponding reference target volume in a reference image; generatinga second treatment plan based on the accepted target volume and a set ofautomatically generated and automatically selected optimizationparameters; and determining, by the user, which treatment plan to use.

In exemplary embodiments, the optimization parameters are automaticallygenerated using previously set clinical goals.

In exemplary embodiments, the optimization parameters are continuouslymodified and automatically selected without the user's input.

In exemplary embodiments, the generating of the first treatment planincludes: determining a reference treatment isocenter location for thereference image; determining an acquisition isocenter location for thetreatment session image; automatically aligning the accepted generatedtarget volume in the treatment session image with the correspondingreference target volume in the reference image; determining differencebetween location of the reference treatment isocenter and theacquisition isocenter; determining treatment session isocenter locationby applying the determined difference to the acquisition isocenterlocation; and using the treatment session isocenter location as input toa plan generation algorithm to generate the second treatment plan.

In exemplary embodiments, the generating of the second treatment planincludes using an optimization parameter optimized plan generationalgorithm to generate a plan and optimizing the generated plan usinginformation contained in a reference treatment plan associated with thereference target volume.

In exemplary embodiments, the generating of the target volume in thetreatment session image includes automatically propagating the referencetarget volume from the reference image to the treatment session imageusing structure-guided deformable registration. The structure-guideddeformable registration can be a deformable registration that is guidedby one or more structures that are present in the reference image andthe treatment session image. The one or more structures in the treatmentsession image can be generated by one of a manual, automatic, or acombination of manual and automatic delineations, and/or by propagatingthe one or more structures from the reference image by deformable and/orrigid deformations. The one or more structures include anatomicalstructures that influence one of a shape, size, or location of thetarget volume, and/or non-volumetric structures.

In exemplary embodiments, the method further comprises verifying, by theuser, that the one or more structures are acceptable prior to being usedto guide the propagation of the reference target volume to the treatmentsession image.

In disclosed embodiments, the determining whether to accept thegenerated target volume includes: presenting the reference imageincluding the reference target volume and the treatment session imageincluding the generated target volume to the user for comparison; andthe user verifying that the generated target volume in the treatmentsession image represents a same anatomical region as the referencetarget volume in the reference image.

An adaptive therapy workflow for generating a session patient model andselecting a treatment plan for the treatment session is also disclosed,the workflow, comprising: obtaining a set of directives, the directivesincluding information relating to a planned treatment of a patient;using the set of directives to guide the adaptive workflow to generate asession patient model in a step-wise fashion starting with the mostvariable anatomy; using directives from the set of directives tocontinuously and automatically optimize a treatment plan generated forthe session model thereby obtaining an adapted plan for the treatmentsession; using the generated session model to automatically transfercontrol points of the planned treatment thereby generating a scheduledplan for the treatment session; and using directives from the set ofdirectives to allow a user to select the treatment plan appropriate forthe treatment session.

A system configured to perform the method steps as disclosed herein isalso disclosed.

A system including a computer processing device configured to execute asequence of programmed instructions embodied on a computer-readablestorage medium, the execution thereof causing the system to execute themethod steps disclosed herein is also disclosed.

A non-transitory computer-readable storage medium upon which is embodieda sequence of programmed instructions for the generation of day to daytreatment images to be used in adaptive radiation therapy, and acomputer processing system that executes the sequence of programmedinstructions embodied on the computer-readable storage medium are alsodisclosed. Execution of the sequence of programmed instructions cancause the computer processing system to execute the adaptive workflowdescribed herein.

Objects and advantages of embodiments of the disclosed subject matterwill become apparent from the following description when considered inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a simplified schematic diagram of a radiation therapy system,according to various embodiments of the disclosed subject matter.

FIG. 2 is a simplified illustration for using the radiation therapysystem of FIG. 1 for on-couch adaptive radiation therapy, according tovarious embodiments of the disclosed subject matter.

FIGS. 3-5 are workflow diagrams for use in on-couch adaptive radiationtherapy, according to various embodiments of the disclosed subjectmatter.

FIG. 6 is a process flow diagram for a session patient model generation,according to various embodiments of the disclosed subject matter.

FIG. 7 is a process flow diagram for generating different treatmentplans for a session patient model, according to various embodiments ofthe disclosed subject matter.

FIG. 8 is a process flow diagram for determining a treatment isocenter,according to various embodiments of the disclosed subject matter.

FIG. 9 is a process flow diagram for selecting a treatment plan,according to various embodiments of the disclosed subject matter.

FIG. 10 is a process flow diagram for generating influencer structureson a treatment session image, according to various embodiments of thedisclosed subject matter.

FIG. 11 illustrates a screen shot of an exemplary contour verificationprocess display.

FIG. 12 is a process flow diagram for image registration and DVFgeneration, according to various embodiments of the disclosed subjectmatter.

FIG. 13 is a process flow diagram for propagated session structureevaluation, according to various embodiments of the disclosed subjectmatter.

FIG. 14 is an on-couch adaptive workflow, according to variousembodiments of the disclosed subject matter.

FIG. 15A illustrates a screen shot of an exemplary bone structurematching process, according to various embodiments of the disclosedsubject matter.

FIG. 15B illustrates a screen shot of an exemplary propagated targetvolume verification process, according to various embodiments of thedisclosed subject matter.

FIG. 16 is an exemplary sagittal CT slice showing a target volume andinfluencer structures in a female pelvis.

FIG. 17A is an exemplary sagittal CT slice showing a motion range of aninfluencer structure.

FIGS. 17B-17D are exemplary axial CT slices showing the motion range ofa target volume and influencer structure.

FIG. 18 is a conventional adaptive workflow diagram.

DETAILED DESCRIPTION

Referring to FIG. 1, an exemplary radiation therapy system 100 is shownthat can be used in adaptive radiation therapy as shown in FIG. 2. Theradiation therapy system 100 can provide radiation to a patient 110positioned on a treatment couch 112 and can allow for the implementationof various radiation dose verification protocols. The radiation therapycan include photon-based radiation therapy, particle therapy, electronbeam therapy, or any other type of treatment therapy.

In an embodiment, the radiation therapy system 100 can include aradiation treatment device 101 such as, but not limited to, a LINACoperable to generate one or more beams of megavolt (MV) X-ray radiationfor treatment. The LINAC may also be operable to generate one or morebeams of kilovolt (kV) X-ray radiation, for example, for patientimaging. The system 100 has a gantry 102 supporting a radiationtreatment head 114 with one or more radiation sources 106 and variousbeam modulation elements, such as, but not limited to, flattening filter104 and collimating components 108. The collimating components 108 caninclude, for example, a multi-leaf collimator (MLC), upper and lowerjaws, and/or other collimating elements. The collimating components 108and/or the flattening filter 104 can be positioned within the radiationbeam path by respective actuators (not shown), which can be controlledby controller 116.

The gantry 102 can be a ring gantry (i.e., it extends through a full360° arc to create a complete ring or circle), but other types ofmounting arrangements may also be employed. For example, a static beam,or a C-type, partial ring gantry, or robotic arm can be used. Any otherframework capable of positioning the treatment head 114 at variousrotational and/or axial positions relative to the patient 110 may alsobe used.

In an embodiment, the radiation therapy device is a MV energy intensitymodulated radiation therapy (IMRT) device. The intensity profiles insuch a system are tailored to the treatment requirements of theindividual patient. The IMRT fields are delivered with MLC 108, whichcan be a computer-controlled mechanical beam shaping device attached tothe head 114 and includes an assembly of metal fingers or leaves. Foreach beam direction, the optimized intensity profile is realized bysequential delivery of various subfields with optimized shapes andweights. From one subfield to the next, the leaves may move with theradiation beam on (i.e., dynamic multi-leaf collimation (DMLC)) or withthe radiation beam off (i.e., segmented multi-leaf collimation (SMLC)).

Alternatively, or additionally, the radiation therapy device 101 can bea tomotherapy device where intensity modulation is achieved with abinary collimator (not shown) which opens and closes under computercontrol (e.g., control 116). As the gantry 102 continuously rotatesaround the patient 110, the exposure time of a small width of the beamcan be adjusted with opening and closing of the binary collimator,allowing radiation 120 to be directed to a portion of the body of thepatient 110 and delivered to a region of interest 122 through the mostdesirable directions and locations of the patient 110. The region ofinterest is a two-dimensional area and/or a three-dimensional volumethat is desired to receive the radiation and it may be referred to as atarget or target region or target volume. Another type of region ofinterest is a region of risk. If a portion includes a region of risk,the radiation is diverted from the region of risk. The patient 110 mayhave more than one target region that needs to receive radiationtherapy.

Alternatively, or additionally, the radiation therapy device can be ahelical tomotherapy device, or a simplified intensity modulated arctherapy (SIMAT) device, a volumetric modulated arc therapy (VMAT)device, or a volumetric high-definition (or hyperarc) therapy (HDRT). Ineffect, any type of IMRT device can be employed as the radiation therapydevice 101 of system 100, and can also include an on-board volumetricimaging, which can be used to generate in-treatment image data generatedduring a treatment session.

For example, embodiments of the disclosed subject matter can be appliedto image-guided radiation therapy (IGRT) devices, which usescross-sectional images of a patient's internal anatomy taken during theradiation therapy treatment session (i.e., in-treatment images) toprovide information about the patient's position. Frequent two orthree-dimensional imaging during the radiation treatment is used todirect the therapeutic radiation utilizing the imaging coordinates ofthe actual radiation treatment plan. This ensures that the patient islocalized in the radiation treatment system in the same position asplanned, and that the patient is properly aligned during the treatment.Although, the IGRT process involves conformal radiation treatment guidedby specialized imaging tests taken during the planning phase, it doesrely on the imaging modalities from the planning process as thereference coordinates for localizing the patient 110 during treatment.Thus, associated with each image-guided radiation therapy system is animaging system to provide in-treatment (treatment session) images thatare used to set-up the radiation delivery procedure.

In-treatment images can include one or more two or three-dimensionalimages (typically X-ray) acquired at one or more different points duringtreatment. There are a variety of ways to acquire in-treatment images.In certain approaches, distinct independent imaging systems and/orimaging methods are used for acquiring pre-treatment and in-treatmentimages, respectively. For example, a 3D IGRT could include localizationof a cone-beam computed tomography (CBCT) dataset with a planningcomputed tomography (CT) dataset, and a 2D IGRT could include matchingplanar kilovoltage (kV) radiographs or megavoltage (MV) images withdigital reconstructed radiographs (DRRs) obtained from the planning CT.

Another approach is to use portal imaging systems. In portal imagingsystems, a detector is placed opposite the therapeutic radiation sourceto image the patient for setup and in-treatment images. Another approachis X-ray tomosynthesis which is an in-treatment imaging modality for usein conjunction with radiation treatment systems.

Alternatively, the system 100 can include a kilovoltage or a megavoltagedetector operable to receive the radiation beam 120. The radiationtherapy device 101 and the detector can operate as a computed tomography(CT) system to generate CT images of the patient. The images canillustrate the patient's body tissues, organs, bone, soft tissues, bloodvessels, etc. Alternatively, the radiation therapy device can operate asan MRI device to generate images of the patient.

Each type of radiation therapy device can be accompanied by acorresponding radiation plan and radiation delivery procedure.

The controller 200, which can be, but is not limited to, a graphicsprocessing unit (GPU), can include a computer with appropriate hardwaresuch as a processor, and an operating system for running varioussoftware programs and/or communication applications. The controller 200can include software programs that operate to communicate with theradiation therapy device 101, which software programs are operable toreceive data from external software programs and hardware. The computercan also include any suitable input/output (I/O) devices 210, which canbe adapted to allow communication between controller 200 and a user ofthe radiation therapy system 100, e.g., medical personnel. For example,the controller 200 can be provided with I/O interfaces, consoles,storage devices, memory, keyboard, mouse, monitor, printers, scanner, aswell as a departmental information system (DIS) such as a communicationand management interface (DICOM) for storing and transmitting medicalimaging information and related data and enabling the integration ofmedical imaging devices such as scanners, servers, workstations,printers, network hardware, etc.

Alternatively, or additionally, the I/O devices 210 can provide accessto a network (not shown) for transmitting data between controller 200and remote systems. For example, the controller 200 can be networked viaI/O 210 with other computers and radiation therapy systems. Theradiation therapy system 100, the radiation treatment device 101, andthe controller 200 can communicate with a network as well as databasesand servers, for example, a dose calculation server (e.g., distributeddose calculation framework) and/or a treatment planning system 300. Thecontroller 200 may also be configured to transfer medical image relateddata between different pieces of medical equipment.

The system 100 can also include a plurality of modules containingprogrammed instructions (e.g., as part of controller 200, or as separatemodules within system 100, or integrated into other components of system100), which instructions cause system 100 to perform different functionsrelated to adaptive radiation therapy or other radiation treatment, asdiscussed herein, when executed. For example, the system 100 can includea treatment plan module operable to generate the treatment plan for thepatient 110 based on a plurality of data input to the system by themedical personnel, a patient positioning module operable to position andalign the patient 110 with respect to a desired location, such as theisocenter of the gantry, for a particular radiation therapy treatment,an image acquiring module operable to instruct the radiation therapysystem and/or the imaging device to acquire images of the patient 110prior to the radiation therapy treatment (i.e., pre-treatment/referenceimages used for treatment planning and patient positioning) and/orduring the radiation therapy treatment (i.e., in-treatment sessionimages), and to instruct the radiation therapy system 100 and/or theimaging device 101 or other imaging devices or systems to acquire imagesof the patient 110.

The system 100 can further include a radiation dose prediction moduleoperable to predict a dose to be delivered to the patient 110 beforecommencement of the radiation treatment therapy, a dose calculationmodule operable to calculate the actual dose delivered to the patient110 during radiation therapy treatment, a treatment delivery moduleoperable to instruct the radiation therapy device 100 to deliver thetreatment plan to the patient 110, a correlation module operable tocorrelate the planning images with the in-treatment images obtainedduring radiation therapy, a computation module operable to reconstructthree-dimensional target volumes from in-treatment images, an analysismodule operable to compute displacement measurements, and a feedbackmodule operable to instruct the controller in real-time to stopradiation therapy based on a comparison of the calculated displacementwith a predetermined threshold value (range).

The system 100 can further include one or more contour generationmodules operable to generate contours of target volumes and otherstructures in pre-treatment (planning, reference) and in-treatment(treatment session) images, an image registration module operable toregister pre-treatment images with subsequent in-treatment images, adose calculation module operable to calculate accumulated dose, acontour propagation module operable to propagate a contour from oneimage to another, a contour verification module operable to verify agenerated contour, a registration deformation vector field generationmodule operable to determine deformation vector fields (DVFs) as aresult of an image deformation process. The system 100 can furtherinclude modules for electron density map generation, isodosedistribution generation, does volume histogram (DVH) generation, imagesynchronization, image display, treatment plan generation, treatmentplan optimization, automatic optimization parameter generation, updatingand selection, and adaptive directives and treatment informationtransfer. The modules can be written in the C or C++ programminglanguage, for example. Computer program code for carrying out operationsas described herein may be written in any programming language, forexample, C or C++ programming language.

The treatment planning system 300 can be used to generate treatmentplans for the radiation treatment device 101 based on image data, suchas CT or CBCT image data, for example. In a typical planning process,qualified medical personnel (physician) manually draw contours on one ormore of the initial reference planning images. These contours delineatethe malignant tumor that is to be irradiated, as well as one or moreother structures, such as organs, tissue, etc. that are susceptible tosubstantial damage from radiation exposure. The planning images can alsobe semi-automatically segmented to delineate the malignant tumor that isto be the target of the irradiation, and any surrounding criticalstructures (OARs) whose irradiation should be limited. Typicaldelineations for the malignant tumor include the gross target volume(GTV), the clinical target volume (CTV), and the planning target volume(PTV). The (GTV) determines the anatomic region which harbors thehighest tumor cell density and requires the highest prescribed dose. The(GTV) is the position and extent of the gross tumor, i.e. what can beseen, palpated or imaged. The (CTV) contains the (GTV), plus a marginfor sub-clinical disease spread which therefore cannot be fully imaged.The (CTV) is very important because this volume must be adequatelytreated to achieve cure. The (PTV) allows for uncertainties in planningor treatment delivery. It is a geometric concept designed to ensure thatthe radiotherapy dose is actually delivered to the CTV. The (PTV) isthus used to compensate for treatment setup uncertainties throughvolumetric expansion of the (CTV) margins. The reference/planning imagescan also illustrate soft tissues, influencer structures, organs, bloodvessels, bones, etc.

Once the physician generates a list of treatment parameters, such as butnot limited to, the targets for which the radiation is to be maximized,targets for which the radiation is to be minimized, a treatment plan isgenerated that also takes into consideration constraints imposed on thetreatment process by the radiation therapy system 100 used fordelivering the radiation to the patient. Additionally, or alternatively,the treatment planning system 300 can use information from other imagingmodalities, such as MRI, PET, etc., and/or other image data forgenerating the treatment plans. The treatment plan is then reviewed bythe physician to ensure that it meets the clinical needs of the patient.

The physician also develops a set of adaptive directives which is a listof parameters/directives/information that describes the intent of theadaptive treatment, namely, the 4D description of the planned treatmentfor the patient. The set of adaptive directives can include informationregarding the planned (reference) dose specification (i.e., Rxprescription), whether adaptive or standard IGRT therapy is to be used,the prescribed clinical goals, such as but not limited to, the targetdose coverage and (OAR) risk dose limits, planned (reference) clinicalgoal values, the planning (reference) image, such as but not limited toa CT image, supporting images with their corresponding registrationinformation (PET, MRI, etc.), the planned (reference) patient model(i.e., the contours of the reference structures, such as the targetvolumes, OARs and other structures on the reference image), the planned(reference) treatment plan (RT Plan), the planned (reference) treatmentplan 3D dose (i.e., RT 3D dose), a list of the reference structures(target volumes, OARs, influencer structures, body outlines), a list ofinfluencer structures of different treatment sites, informationregarding the shapes and location of the planned (reference) structureson the planned (reference) image, as well as any information as to howthe planned (reference) treatment plan was optimized.

The intent of the adaptive radiotherapy is to appropriately modify theradiation treatment plan to account for the temporal changes in theanatomy. As such, images, such as CBCT images for example, obtainedduring a treatment session (i.e., treatment session images) at thetreatment site are sent to the treatment planning system 300 where a newtreatment plan can be adapted to the current anatomy via deformableregistration software and sent back to the radiation therapy system 100for delivery. For on-couch adaptive radiotherapy, the workflow involvedin such adaptive therapy has to be fast enough to be able to adapt theradiation plan to the new anatomy while the patient is still on thecouch.

In order to achieve an accurate on-couch adaptive workflow, first anaccurate session patient model (i.e., contours on the current anatomy)needs to be generated, then the treatment plan properly modified to fitthe new anatomy, then quickly evaluated for application on the patient.The on-couch adaptive workflows described herein achieve this bygenerating a session patient model in a step-wise fashion through aseries of automated steps guided by the set of adaptive directivespreviously determined by a prescriber (i.e., physician, for example)during treatment planning. The set of directives are also used to guidethe generation and the selection of a treatment plan that is mostappropriate for the current anatomy of the patient.

In an on-couch adaptive workflow shown in FIGS. 2-4 and 14, the set ofadaptive directives developed by the prescriber, are either sent to thecontroller 200 to be saved therein and/or are made available during theadaptive therapy session via the departmental information system (DICOM,for example).

The reference image in the set of directives may be an image that wasobtained previously, e.g., in a different imaging session, for the sameor a different patient, that may have occurred on a different day, or onthe same day. The reference image may also be an image of a differentindividual, in which case, image registration can be used to map thepatient image to an atlas patient image. The reference image may also bean image that was artificially created via artificial intelligence (AI)segmentation that does not correspond to any individual.

In an exemplary embodiment, the reference image is a planning imageobtained for the patient 110 during the treatment planning phase. Thereference image can include a set of delineated reference structures,such as one or more target volumes (PTV, CTV, GTV, for example), one ormore affected organs (OARs, for example), one or more anatomicalstructures of interest (body outlines, for example), as well as one ormore influencer structures (organs and/or non-volumetric structures, forexample).

For example, the reference image may contain a target volume includingthe primary tumor (i.e., primary target), the primarily affected organ(i.e., primary organ), and a region where invasion of lymph nodes hasbeen observed or is to be expected (i.e., nodal target). Alternatively,the reference image may contain a target volume including the primarytarget, the primary organ, and the nodal target, and may contain one ormore contours delineating other anatomical structures of interest.Alternatively, the reference image may include a plurality of targetvolumes. Alternatively, the reference image may contain a plurality oftarget volumes, with each target volume including a different type ofprimary and/or nodal target and/or primary organ. Alternatively, thereference image may include a plurality of target volumes and one ormore primary organs and one or more other anatomical structures ofinterest.

The reference image can also include contours of structures thatinfluence one or more of the shape, size, and location of one or more ofthe primary target, the nodal target, the primary organ, and the otheranatomical structures of interest. These influencer structures caninclude structures, such as organs, that generally move and/or exhibitlarge deformations and/or movements from day to day, as well as othernon-volumetric structures, such as points or 2D lines, for example, thatdescribe an anatomical situation. The contours of these influencerstructures generally do not propagate well from the planning image ontothe treatment session image due to their highly deformable nature.Information regarding the shapes and locations of these referencestructures in the reference image are also included in the set ofadaptive directives as the reference patient model.

FIG. 16 shows an exemplary reference image 400 for a cervix uteritreatment. The reference image 400 includes a contour 401 delineatingthe primary and nodal targets as the target volume, and a set ofcontours 402, such as a contour 402A delineating the bladder, a contour402B delineating the uterus, and contours 402C delineating the rectum,as the influencer structures. Reference image 400 may also include otherstructures of interest, such as body outlines or femoral heads, etc., asshown in FIG. 16.

FIG. 17A shows the bladder 402A having moved from a first location(402A) to a second location (402A′), and FIGS. 17B-17D show how a targetvolume 401 moves from day to day due to the movement of the uterus 402B.

Although these images show specific structures used for the targetvolume and influencer structures, it is to be understood that the targetvolumes and influencer structures are not limited to these specificstructures, and that any structures may be used that are relevant for aspecific treatment, such as, but not limited to, the treatment ofprostate, pancreas, rectal cancer, etc. Also, the influencer structuresmay also include structures that are non-volumetric structures, such aspoints or 2D lines that help describe an anatomical situation.

In order to adapt a new treatment plan to the current anatomy, once theset of adaptive directives are made available at the treatment site, aradiation technologist (RTT/MTA) who is skilled and trained at reviewinganatomy and plan selection, and who is tasked to deliver the adaptivetreatment on the patient (i.e., the user/adapter), executes the firstlevel of treatment modification, which is setting the patient on thetreatment couch and moving the patient to the imaging position (PatientSetup, S102).

After the patient setup, the next step is to acquire, prior to thetreatment, at the treatment site, one or more treatment session images(Imaging, S103) of the portion of the patient 110 that is of interest,using the radiation imaging device 101. In an exemplary embodiment, thetreatment session image is a 3D or 4D CBCT scan for example obtainedduring a treatment session by irradiating the region of interest of thepatient 110 with radiation 120. This treatment session image may showboney structures of the patient but does not include any delineations oftarget volumes or other structures.

Then, the RTT/MTA initializes the generation of contours of thereference structures on the treatment session image (Contouring, S104)to obtain a treatment session patient model. The details of this guidedcontouring process S104 are shown in FIGS. 5 and 6. The RTT/MTA theninitializes the generation of different plans for the session patientmodel obtained in S104 and the selection of the appropriate plan(S105-S110) to be delivered to the patient (S111). In order to generateaccurate treatment plans, however, an accurate session patient modelneeds to be generated. Namely, the target volumes (CTV, PTV, etc.),OARs, organs, anatomical structures, influencer structures, bodyoutlines, etc. that were present in the reference image need to becorrectly shown in the treatment session image, so that no furtherrevisions/recontouring and no electron density map error corrections areneeded to be made by a physician. The details of the plan generation andselection process (S105-S110) are shown in FIGS. 7-9. Optionally, thetreatment delivery can be evaluated/monitored offline by a same ordifferent physician.

As shown in FIGS. 3-4, and 14, the on-couch adaptive workflow is guidedby the set of directives made available in S101, so as to perform aseries of automated steps by the RTT/MTA that allow for the generationof an accurate session patient model that need not be reviewedseparately by a physician, and which can generate accurate treatmentplans that the RTT/MTA can select from without additional physicianinput.

Contouring S104

As shown in FIG. 6, to generate a session patient model that accuratelyreflects the shape and location of the reference structures on thetreatment session image, namely, a treatment session image including thecontours of the structures that were present in the reference image(i.e., the reference structures), the session patient model is built ina step-wise fashion.

Step 1—Match Bones

As a first, optional step S200, the treatment session image obtained instep S103, which is an image that shows an anatomy of interest and mayshow one or more boney structures, is compared with the reference imagewhich also includes a corresponding set of boney structures. For this,the RTT/MTA is provided on a console display with the reference imageand the treatment session image as shown in FIG. 15A for comparison andanalysis. If the RTT/MTA decides that the boney structures in the twoimages do not match (S201), the RTT/MTA initiates treatment couchadjustment in S202 to correct the positional error found by registeringthe two images. When the boney structures match, the RTT/MTA initiatesthe generation of the influencer structures on the treatment sessionimage in S203. Alternatively, this step S200 could be performed in thebackground, without displaying information to the RTT/MTA.Alternatively, this step S200 could be performed after Step 3 describedbelow.

Step 2—Generate and Evaluate Influencer Structures

The generation of the influencer structures (S203) on the treatmentsession image obtained in S103 is shown in FIG. 10. As shown in FIG. 10,a treatment session image 500 obtained from the Imaging step S103 doesnot contain any delineations. Using the list of influencer structuresthat are included in the set of adaptive directives and which list isavailable to the RTT/MTA, the RTT/MTA initiates, via options madeavailable to the RTT/MTA on the console, one of an automatic, manual, ora combination of automatic and manual delineation of the influencerstructures in the list of influencer structures that are appropriate forthe treatment site. For example, if the patient is treated for cervixuteri cancer, the influencer structures to be picked from the list ofinfluencer structures for delineation are the bladder, the rectum andthe uterus. Further, if other non-volumetric influencer structures werepresent in the reference image, these influencer structures are alsodelineated in the treatment session image 500.

As shown in FIG. 10, when initiated by the RTT/MTA, the selectedinfluencer structures can be automatically delineated using variousavailable automatic segmentation software (algorithms) executed by thecontour generation module 118, that automatically detect the influencerstructures and draw the respective contours 502. The influencerstructures can also be created from the treatment session image datadirectly using artificial intelligence (AI) based segmentation, imageanalysis, shape models, etc. The influencer structures can also bederived from the previously segmented reference image via deformable orrigid image registration. The delineation can also be done manually bythe RTT/MTA via an interactive graphical interface (GUI) for example,that allows the user to identify and draw the contours 502 on thetreatment session image 500.

Once the contours 502 of the influencer structures are drawn on thetreatment session image 500, the contours 502 may be further reviewed bythe RTT/MTA using various available contour verification tools presentin the verification module 119 of the GUI, for example (S204). Using theGUI, the user can modify, delete, redraw the automatically generatedcontours 502 (S205), to establish consistency between the influencerstructures in the reference image and the influencer structures in thetreatment session image 500.

An exemplary verification process is shown in FIG. 11, where thecontours for the bladder 502A, the uterus 502B, and the rectum 502C arereviewed from different views on a display device of a GUI. Theverification module 119 may also support the user identifying locationswhere a correction to the influencer structures may be necessary (i.e.,Sanity checks). The verification module 119 may also support the userverifying the correctness of the influencer structures by displaying areference, such as the reference image, an anatomy atlas, contouringguidelines, etc.

Since the influencer structures deform significantly, the location ofthe influencer structures on the treatment session image 500 may bedifferent from the location of the same influencer structures in thereference image 400, as shown in FIG. 17A.

Once the influencer structures are corrected (502′), if needed, by theRTT/MTA and the RTT/MTA determines that the patient anatomy of eachinfluencer structure is captured correctly on the treatment sessionimage 500, the RTT/MTA initiates the propagation of the rest of thereference structures (except the influencer structures) included in thereference image (S206). Namely, the RTT/MTA initiates propagation of thetarget volumes (CTV, PTV, etc.), OARs, and other anatomical structuresof interest besides the influencer structures that were present in thereference image 400 (i.e., propagation of selective referencestructures).

Step 3—Propagate and Evaluate Structures

The propagation is done using a structure-guided deformable registrationalgorithm (Structure-Guided DIR) that registers the image data of thereference image 400 with the image data of the treatment session image500, and which generates, as a result, one or more deformable vectorfields (DVFs) 203 using a DVF determination module 202 (see FIG. 12).The DVF 203 can then be used to propagate the target volumes (CTV, PTV,etc.), OARs, and other anatomical structures of interest from thereference image 400 to the treatment session image 500 using a contourpropagation module 205.

The guided deformable registration is a deformable registration that isguided by the relationship between the influencer structures that arepresent in the reference image 400 and those that were generated in thetreatment session image 500. The structure-guided deformableregistration is a registration process wherein the influencer structuresare used as inputs into a deformable registration algorithm to guide thedeformation process, wherein the guiding is realized by incorporating aconstraint in the deformable registration algorithm to enable matchingof influencer structures present in the reference image with the sameinfluencer structures generated in a subsequent treatment image. Byapplying the structure-guided deformable registration between the twoimages, the structure-guided deformable registration algorithm enablesobtaining one or more deformation vector fields (DVFs) and, using theone or more deformation vector fields (DVFs), to accurately propagateselected reference structures from the reference image to the treatmentsession image. An exemplary structure-guided deformable registrationprocess that can be applied to propagate the reference structures isdisclosed in detail in U.S. patent application Ser. No. 16/144,253.

The one or more deformable vector fields (DVFs) 203 can also be used fordose accumulation determination by the dose calculation module 204 andto calculate the envelope isodose for the propagated structures. Thecontroller 116 then initiates the contouring of the propagatedstructures (i.e., OARs, CTV, PTV) based on the propagated image data.

Once the selective reference structures (target volumes, OARs, etc.) arepropagated in S206, the RTT/MTA is prompted to evaluate one or more ofthe propagated structures in S207 shown in FIG. 13. In order to do that,the RTT/MTA is presented with the session treatment image 500 containingthe generated influencer structures as well as the propagated structures(i.e., the contours of the session structures overlaid on the treatmentsession image) alongside the reference image 400 containing thereference structures (i.e., the contours of the reference structuresoverlaid on the reference image) on the console display, as shown inFIG. 15B. Optionally, overlaid on the two images are body outlines, aswell as a radiation dose representation (Isodose lines, color washes,etc.).

The RTT/MTA utilizes the presented information to verify that thecreated target volumes, namely, the target volumes propagated to thetreatment session image 500, represent the correct anatomical regions ofthe patient. In other words, the RTT/MTA compares the treatment sessionimage which now has the generated influencer structures and thepropagated structures (i.e., the session structures) overlaid thereonwith the reference image which has the reference structures overlaidthereon, and using information such as the shapes and positions of thereference structures on the reference image (i.e., the reference patientmodel) included in the set of adaptive directives, calculates thepositions and shapes of the propagated target volumes on the treatmentsession image, and determines whether the propagated target volumes(CTV, PTV, GTV, etc.) represent the same anatomical regions of thepatient as those represented by the reference structures in thereference image.

The system 101 supports the RTT/MTA with this assessment by allowing theRTT/MTA to synchronize the views of the two images, letting the RTT/MTAselect which information is displayed in the views, providing tools(software, hardware), such as measurement tools and volume informationtools, for example, to automatically assess the shapes, positions, andlocations of the propagated target volumes (CTV, PTV, GTV, etc.) on thetreatment session image, and letting the RTT/MTA select which tools touse.

Optionally, the RTT/MTA is also provided with tools (hardware, software)to assess the other propagated structures (OARs, body outlines, etc.) onthe treatment session image.

Optionally, the RTT/MTA is also provided with tools (hardware, software)that automatically detects irregularities in the propagated structures.

Optionally, the RTT/MTA is also provided with tools that, automaticallyor otherwise, guide the RTR/MTA to the locations where theirregularities are detected.

Optionally, the RTT/MTA may also use radiation doses calculated usingthe radiation prescription in the set of adaptive directives obtained inS101 and an adapted patient model created based on the generatedinfluencer structures in the treatment session image and the calculatedpositions and shapes of the propagated target volumes on the treatmentsession image.

Based on the result of the assessment of the propagated target volumes,the RTT/MTA is presented with several options. If the result of theassessment is that the propagated target volumes are correct, theRTT/MTA accepts the propagated target volumes, in which case, thegenerated and the propagated contours, namely, the contours of thegenerated influencer structures and the contours of the propagatedstructures (together the Session Structures) are accepted as beinganatomically consistent with the initially defined contours of thereference structures in the reference image, but adapted to thelocations of the current anatomies. The shape and locations of thecontours of the session structures on the treatment session image arealso determined.

If the result of the assessment is that the propagated target volumesare not correct, the RTT/MTA is presented with several options to choosefrom to move forward. One option is to drop out (S210), another optionis to select an expert mode (S208). The expert mode S208 allows theRTT/MTA to either apply a contour correction algorithm built into thesystem to automatically correct the propagated contours (S209), or tocall on an expert located onsite or offline to automatically or manuallycorrect the propagated contours (S209). The RTT/MTA is also providedwith the option to make on-the-fly corrections in the propagatedcontours, and/or use additional supporting images (PET, MRI) from theset of adaptive directives to help in the reviewing process.

Upon acceptance of the propagated target volumes as correctly showingthe anatomies of the reference patient model, the contours of thesession structures on the treatment session image are accepted as thesession patient model.

Plan Selection S107-S110

Generate Scheduled Plan S105

Once accepted, the propagated target volumes of the session patientmodel can be used to generate a scheduled plan, as shown in FIGS. 7-8.The RTT/MTA through the console can initiate the scheduled plangeneration.

Initially, when starting the adaptive workflow, the patient 110 is setupon the treatment couch 112 the same way as for a standard radiationtherapy treatment. Therefore, after positioning the patient 110 on thetreatment couch 112, the patient 110 is taken to the radiation therapysystem 100 isocenter, using traditional skin marks indicating thelocation of the isocenter.

When the treatment session image (i.e., the CBCT image, for example) 500is acquired in S103, the center of the treatment session image 500corresponds to the acquisition isocenter. If the patient 110 is treatedby aligning the acquisition isocenter with the system isocenter, thepatient 110 will not be treated correctly, since, as shown in FIG. 8,the session target volume does not align with the reference targetvolume. In order to determine the correct treatment isocenter (S301),once the RTT/MTA accepts the propagated target volume (i.e., the sessiontarget volume) on the treatment session image 500 (step S207), thesystem 100 is prompted to automatically align (S300) the referencetarget volume of the reference patient model obtained from S101 to thesession target volume of the session patient model obtained in S104.

As shown in FIG. 8, this alignment provides the difference between theacquisition isocenter and the reference treatment isocenter. From thisdifference, the translation (X, Y, Z) values, namely, by how much in theX, Y, and Z direction does the patient 110 need to be moved for the twoisocenters to align, is calculated. The calculated translation valuesare then applied to the acquisition isocenter so that the treatmentisocenter location is determined in S301. This treatment isocenterlocation is then provided in S302 to a dose volume calculation algorithmwhich, using the information regarding the reference plan from S101,generates a scheduled plan. The scheduled plan also contains informationregarding the new treatment couch 112 location, which will becommunicated to the treatment delivery system 101 if the scheduled planis selected for treatment. When the scheduled treatment plan containsnew treatment couch position, it forces the RTT/MTA to apply (move thetreatment couch 112) to the new location.

Generate Scheduled Dose Matrix S106

The scheduled plan generated in S105 is next applied to a dosecalculation algorithm in S303 to calculate in S106 the radiation dose tobe applied to the session target volume according to the scheduled plan.The scheduled dose matrix so generated (S304) is then sent together withthe generated scheduled plan and scheduled isodose values to a displaydevice to be displayed for the RTT/MTA in S109.

Generate Adapted Plan S107

Once accepted in S207, the propagated target volumes together with theother propagated structures (OARs, etc.) (together the propagatedsession structures) of the session patient model can be used to generatean adapted plan, as shown in FIGS. 5 and 7. The RTT/MTA through theconsole can initiate the adapted plan generation.

To generate the adapted plan, the propagated session structures togetherwith a synthetic image of the patient (i.e., synthetic CT, for example)obtained in S103′ are used as inputs to an automated plan generationalgorithm in S400. The plan generation algorithm combines severalcomponents from existing components (Photon Optimization algorithm(PO-GPU) for VMAT and IMRT, SmartLMC algorithm for leaf sequencing,RapidP Ian for DVH-estimation, FTDC-GPU for optimization dosecalculation, AcurosXP-GPU for final dose calculation), and additionally,to support the automated adaptive workflow, further includes anadditional component that allows for the automatic generation, automaticselection, and automatic continuous modification of optimizationparameters by which the algorithm S401 and ultimately the generated planS402 are optimized. This relieves the RTT/MTA from having tohimself/herself provide optimization specific parameters (objectives,optimization structures, options, normal tissue handling, etc.) on acase by case basis. This not only reduces the time needed for the plangeneration but also reduces potential errors that could be introduced bythe RTT/MTA picking the wrong parameters.

To generate the synthetic CT, the treatment session image generated inS103 is further registered with the reference image including thereference structures obtained from S101. The image registration caninclude one or more of a rigid registration and elastic deformableregistration (elastic DIR), and/or one or more of an atlas-basedsegmentation, bone segmentation, etc. The result of the rigidregistration between the two images is used as an input to an elasticdeformable registration algorithm (elastic DIR), and the registrationdata output from the elastic DIR together with the data regarding thedetected body outlines from the treatment session image of S103 are usedas inputs to a synthetic CT generation algorithm, as shown in FIG. 14.

The automated plan generation algorithm of S400 takes the createdsynthetic CT and the propagated session structures as inputs to modifythe reference plan based on the new anatomy. The original treatmentinstruction, namely, the physician defined set of clinical goalscontained in the set of adaptive directives in S101, are used in S401 toautomatically generate a set of optimization parameters/criteria for theadaptive plan generation. The plan generation algorithm uses theautomatically generated and selected optimization parameters to generatea plan in S402. The so generated plan in S402 is further automaticallyoptimized using additional information, including reference planoptimization parameters, associated with the reference plan, which areincluded in the set of directives obtained in S101. The reference planis used in S402 with the aim of optimizing the generated plan to a dosedistribution of similar dosimetric characteristics as the dosedistribution of the reference plan.

The information regarding the reference plan parameters used to optimizethe generated plan in S402 also include radiation treatment system 100delivery characteristics such as beam angles and monitor units, forexample. These optimization parameters are used so as to obtain a newplan, namely, an adapted plan, which meets the original clinical goalsto a similar degree as the original treatment plan.

Generate Adapted Dose Matrix S108

The adapted plan generated in S107 is next applied to a dose calculationalgorithm in S403 to calculate in S108 the radiation dose to be appliedto the session target volume according to the adapted plan. The adapteddose matrix so generated (S404) is then sent together with the generatedadapted plan and adapted isodose values to the display device to bedisplayed for the RTT/MTA in S109.

Both the scheduled and the adapted plans can also be checked andvalidated before treatment delivery. Plan checks can be validated toensure delivery on the treatment device using DICOM artifacts (referenceplan, images, structures, doses, etc.), which are provided forindependent quality assurance. Optionally the independent validationapplication communicates back to the adaptive workflow application withthe results of the validation (QA).

Plan Display and Selection S109-S110

The scheduled plan together with the calculated scheduled dose matrix aswell as the adapted plan together with the calculated adapted dosematrix are displayed for the RTT/MTA for selection, as shown in FIGS. 5and 9. The RTT/MTA is also provided with one or more tools (hardware,software, etc.) to evaluate the scheduled plan and the adapted plan. Thetools may include tools that provide Isodose distribution of thereference plan on the reference image, Isodose distribution of thescheduled plan on the treatment session image, Isodose distribution ofthe adapted plan on the session treatment image, Dose Volume Histograms(DVHs) of the reference plan, the scheduled plan and the adapted plan.The tools may also include tools for clinical goals evaluation toprovide the reference clinical goal values, and the scheduled andadapted plan clinical actual values. Optionally, the RTT/MTA may bepresented with other images, such as multi modal images (PET, MRI), thatmay help in the plan selection process. The RTT/MTA can select whatinformation to be displayed on display S109 and what additional tools touse to help in the selection of the most appropriate plan.

Equipped with these tools and choices, the RTT/MTA determines in S500whether the scheduled plan is the acceptable plan for the currenttreatment session. If the scheduled plan is acceptable, the RTT/MTAselects in S501 to deliver the scheduled plan in S502 for treatmentdelivery in S111. If the RTT/MTA determines in S500 that the scheduledplan is not acceptable, the RTT/MTA selects the adapted plan in S503 forevaluation. If the RTT/MTA determines in S504 that the adapted plan isthe appropriate plan for the current treatment session, then the RTT/MTAselects in S505 to deliver in S506 the adapted plan for treatmentdelivery in S111. If the RTT/MTA determines in S504 that the adaptedplan is also not acceptable for the current treatment session, then theRTT/MTA can choose to drop out in S507 or to use a contingency plan inS508 for treatment delivery. The contingency plan is a plan that iscreated based on the reference plan but for previously calculatedaverage target volume positions (average CTV position) that includegreater dose margins than those calculated for the reference targetvolumes.

The RTT/MTA uses the reference clinical goals and reference clinicalgoal values in the set of directives obtained in S101 to quantitativelyassess if the scheduled and/or the adapted plan is medically necessaryfor the treatment session of the day (i.e., the current treatmentsession). For this, the reference clinical goal values and the actualclinical values for the scheduled and adapted treatment plans arepresented to the RTT/MTA on the display, and the RTT/MTA selects thetreatment plan that provides the clinical values closest to thereference clinical goal values.

Once the user selects a treatment plan, the radiation treatment willproceed in S111 according to the selected plan. The prescribingphysician (MD) which generated the original treatment plan, the adaptivedirectives, and the adaptive workflow, or any other qualified physician,can review and/or monitor the treatment delivery offline, as shown inFIG. 5.

It is thus apparent that the disclosed subject matter enables for theuse of a set of directives to guide an adaptive workflow in order togenerate a session patient model and to select an appropriate treatmentplan for the treatment session, as shown in FIG. 14. The adaptiveworkflow comprises obtaining a set of directives, the directivesincluding information relating to a planned treatment of a patient;using the set of directives to guide the adaptive workflow to generate asession patient model in a step-wise fashion starting with the mostvariable anatomy; using directives from the set of directives tocontinuously and automatically optimize a treatment plan generated forthe session model thereby obtaining an adapted plan for the treatmentsession; using the generated session model to automatically transfercontrol points of the planned treatment thereby generating a scheduledplan for the treatment session; and using directives form the set ofdirectives to allow a user to select the treatment plan appropriate forthe treatment session.

It is thus also apparent that the disclosed subject matter also enablesa system to perform the guided adaptive workflow as described herein.

It will be appreciated that the aspects of the disclosed subject mattercan be implemented, fully or partially, in hardware, hardware programmedby software, software instruction stored on a computer readable medium(e.g., a non-transitory computer readable medium), or any combination ofthe above.

For example, components of the disclosed subject matter, includingcomponents such as a controller, process, or any other feature, caninclude, but are not limited to, a personal computer or workstation orother such computing system that includes a processor, microprocessor,microcontroller device, or is comprised of control logic includingintegrated circuits such as, for example, an application specificintegrated circuit (ASIC).

Features discussed herein can be performed on a single or distributedprocessor (single and/or multi-core), by components distributed acrossmultiple computers or systems, or by components co-located in a singleprocessor or system. For example, aspects of the disclosed subjectmatter can be implemented via a programmed general purpose computer, anintegrated circuit device, (e.g., ASIC), a digital signal processor(DSP), an electronic device programmed with microcode (e.g., amicroprocessor or microcontroller), a hard-wired electronic or logiccircuit, a programmable logic circuit (e.g., programmable logic device(PLD), programmable logic array (PLA), field-programmable gate array(FPGA), programmable array logic (PAL)), software stored on acomputer-readable medium or signal, an optical computing device, anetworked system of electronic and/or optical devices, a special purposecomputing device, a semiconductor chip, a software module or objectstored on a computer-readable medium or signal.

When implemented in software, functions may be stored on or transmittedover as one or more instructions or code on a computer-readable medium.The steps of a method or algorithm disclosed herein may be embodied in aprocessor-executable software module, which may reside on acomputer-readable medium. Instructions can be compiled from source codeinstructions provided in accordance with a programming language. Thesequence of programmed instructions and data associated therewith can bestored in a computer-readable medium (e.g., a non-transitory computerreadable medium), such as a computer memory or storage device, which canbe any suitable memory apparatus, such as, but not limited to read-onlymemory (ROM), programmable read-only memory (PROM), electricallyerasable programmable read-only memory (EEPROM), random-access memory(RAM), flash memory, disk drive, etc.

As used herein, computer-readable media includes both computer storagemedia and communication media, including any medium that facilitatestransfer of a computer program from one place to another. Thus, astorage media may be any available media that may be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia may comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that may be used to carry or store desired program code inthe form of instructions or data structures and that may be accessed bya computer.

Also, any connection is properly termed a computer-readable medium. Forexample, if the software is transmitted from a website, server, or otherremote source using a transmission medium (e.g., coaxial cable, fiberoptic cable, twisted pair, digital subscriber line (DSL), or wirelesstechnologies such as infrared, radio, and microwave), then thetransmission medium is included in the definition of computer-readablemedium. Moreover, the operations of a method or algorithm may reside asone of (or any combination of) or a set of codes and/or instructions ona machine readable medium and/or computer-readable medium, which may beincorporated into a computer program product.

One of ordinary skill in the art will readily appreciate that the abovedescription is not exhaustive, and that aspects of the disclosed subjectmatter may be implemented other than as specifically disclosed above.Indeed, embodiments of the disclosed subject matter can be implementedin hardware and/or software using any known or later developed systems,structures, devices, and/or software by those of ordinary skill in theapplicable art from the functional description provided herein.

In this application, unless specifically stated otherwise, the use ofthe singular includes the plural, and the separate use of “or” and “and”includes the other, i.e., “and/or.” Furthermore, use of the terms“including” or “having,” as well as other forms such as “includes,”“included,” “has,” or “had,” are intended to have the same effect as“comprising” and thus should not be understood as limiting.

Any range described herein will be understood to include the endpointsand all values between the endpoints. Whenever “substantially,”“approximately,” “essentially,” “near,” or similar language is used incombination with a specific value, variations up to and including 10% ofthat value are intended, unless explicitly stated otherwise.

The terms “system,” “device,” and “module” have been usedinterchangeably herein, and the use of one term in the description of anembodiment does not preclude the application of the other terms to thatembodiment or any other embodiment.

Many alternatives, modifications, and variations are enabled by thepresent disclosure. While specific examples have been shown anddescribed in detail to illustrate the application of the principles ofthe present invention, it will be understood that the invention may beembodied otherwise without departing from such principles. For example,disclosed features may be combined, rearranged, omitted, etc. to produceadditional embodiments, while certain disclosed features may sometimesbe used to advantage without a corresponding use of other features.Accordingly, Applicant intends to embrace all such alternative,modifications, equivalents, and variations that are within the spiritand scope of the present invention.

The invention claimed is:
 1. A system for implementing an automatedworkflow for an adaptive radiation therapy session of a patient,comprising: a computer processing system configured to: obtain a set ofdirectives, the set of directives including information representing aplanned treatment for the patient; and using the set of directives,perform a series of automated steps to: generate a session patient modelin a step-wise fashion; generate a first and a second treatment plan forthe session patient model; and display the first and second treatmentplans to a user, wherein the computer processing system is furtherconfigured to allow the user to select from the displayed treatmentplans the treatment plan that is appropriate for a current treatmentsession.
 2. The system of claim 1, wherein the set of directivesincludes information regarding planned radiation dose, planned clinicalgoals, planned clinical goal values, reference patient model, referencetreatment plan, list of influencer structures, and one or more referenceimages, wherein the influencer structures include anatomical structuresthat influence other anatomical structures of the patient.
 3. The systemof claim 2, wherein in order to generate the session patient model in astep-wise fashion, the computer processing system: generates a treatmentsession image of a portion of the patient, the treatment session imagecontaining an anatomy of interest; generates one or more influencerstructures from the list of influencer structures on the treatmentsession image; evaluates the generated influencer structures based onone or more directives of the set of directives; propagates targetstructures of the reference image to the treatment session image uponacceptance of the generated influencer structures; evaluates thepropagated target structures based on one or more directives of the setof directives; and accepts the treatment session image including theinfluencer structures and the propagated structures as the sessionpatient model upon acceptance of the propagated target structures. 4.The system of claim 3, wherein the target structures include: a firstset of target structures representing contours of a primary tumor of thepatient; and a second set of target structures representing contours ofone or more primarily affected organs (OARs) of the patient, and theinfluencer structures include: a first set of influencer structuresrepresenting contours of one or more organs that affect one or more of ashape, size or location of one or more of the target structures; and asecond set of influencer structures representing contours ofnon-volumetric structures.
 5. The system of claim 4, wherein the one ormore influencer structures are generated by one of a manual, automatic,or a combination of manual and automatic segmentation, or by propagatingthe one or more influencer structures from the reference image to thetreatment session image by deformable and/or rigid deformation.
 6. Thesystem of claim 5, wherein the computer processing system employsstructure-guided deformable registration to propagate the targetstructures from the reference image to the treatment session image,wherein the structure-guided deformable registration is deformableregistration that is guided by one or more of the influencer structures.7. The system of claim 3, further configured to allow the user toperform the evaluating, accepting, and selecting.
 8. The system of claim7, wherein, in order to determine whether the one or more influencerstructures are acceptable, the computer processing system: presents, tothe user, the treatment session image including the generated influencerstructures and the reference image including corresponding referenceinfluencer structures for comparison; and allows the user to verify thatthe generated influencer structures correspond to the referenceinfluencer structures.
 9. The system of claim 8, wherein the computerprocessing system is configured to display contouring/segmentationguidelines to the user to be used for the verifying.
 10. The system ofclaim 9, wherein, in order to determine whether the propagated targetstructures are acceptable, the computer processing system: presents, tothe user, the treatment session image including the propagated targetstructures and the reference image including corresponding referencetarget structures for comparison; and allows the user to verify that thepropagated first set of target structures on the treatment session imagerepresent same anatomical regions of the patient as the reference firstset of target structures in the reference image.
 11. The system of claim10, wherein the determining further includes synchronizing the referenceimage with the treatment session image prior to the verifying.
 12. Thesystem of claim 11, wherein the determining further includes usinginformation relating to shapes and positions of the propagated andreference first set of target structures in the treatment session imageand the reference image.
 13. The system of claim 12, wherein thedetermining further includes using information relating to radiationdose representations in the reference image and the treatment sessionimages.
 14. The system of claim 13, wherein the determining furtherincludes using automated tools to detect irregularities in the comparedfirst set of target structures.
 15. The system of claim 14, wherein thedetermining further includes using automated tools to guide the user tolocations where irregularities are detected.
 16. The system of claim 11,wherein when the user determines that the propagated first set of targetstructures are not acceptable, the user can select to correct thepropagated first set of target structures or to default to another userfor correction.
 17. The system of claim 7, wherein the generating of thefirst treatment plan for the session patient model, includes: obtaininga reference isocenter location for the reference patient model from theset of directives; determining an acquisition isocenter location for thesession patient model; aligning the accepted propagated first set oftarget structures in the session patient model with the correspondingreference first set of target structures in the reference patient model;determining a difference between the location of the reference isocenterand the location of the acquisition isocenter; determining a treatmentsession isocenter location by applying the determined difference to theacquisition isocenter location; and using the treatment sessionisocenter location as an input to a plan generation algorithm togenerate the first treatment plan.
 18. The system of claim 17, whereinthe generating of the second treatment plan includes: generating asynthetic image for the patient by registering the treatment sessionimage with the reference image; using the synthetic image and thepropagated target structures as input to a treatment plan generationalgorithm to generate a treatment plan, wherein the plan generationalgorithm includes optimization parameters which are automaticallygenerated based on the planned clinical goals included in the set ofdirectives; and generating the second treatment plan by optimizing thegenerated plan using information relating to the reference treatmentplan included in the set of directives.
 19. The system of claim 18,wherein the computer processing system is configured to automaticallymodify and select the optimization without the user's input.
 20. Thesystem of claim 7, wherein the selecting of the appropriate treatmentplan includes: evaluating whether the first treatment plan is acceptablefor the current treatment session using the clinical goals from the setof directives; selecting the second treatment plan when determined thatthe first treatment plan is not acceptable; evaluating whether thesecond treatment plan is acceptable; and selecting a contingency planwhen determined that the second treatment plan is not acceptable. 21.The system of claim 20, wherein the selecting of the appropriatetreatment session further includes: presenting, to the user, the firstand second treatment plans in a comparison view; illustrating isodosedistribution of the reference plan on the reference patient model,isodose distribution of the first treatment plan on the treatmentsession model, and isodose distribution of the second treatment plan onthe treatment session model; illustrating dose volume histograms of thereference plan, the first treatment plan and the second treatment plan;presenting the planned clinical goal values; presenting actual clinicalvalues for the first and second treatment plans; and selecting thetreatment plan that provides the clinical values closest to the plannedclinical goal values.
 22. The system of claim 21, wherein thedetermining further includes selecting, by the user, a contingency planwhen neither the first and second treatment plans achieve the plannedclinical goals.
 23. The system of claim 3, wherein the computerprocessing system is further configured to evaluate the treatmentsession image using a reference image from the set of directives, thereference image containing a reference bone structure, the treatmentsession image being accepted when a bone structure of interest in thetreatment session image matches the bone structure in the referenceimage.
 24. A non-transitory computer readable storage medium upon whichis embodied a sequence of programmed instructions for the generation ofday to day treatment images to be used in an adaptive radiation therapy,which sequence of programmed instructions when executed, causes acomputer processing system to execute the following steps: obtain a setof directives, the set of directives including information representinga planned treatment for a patient; and using the set of directives,perform a series of automated steps to: generate a session patient modelin a step-wise fashion; generate a first and a second treatment plan forthe session patient model; and display the first and second treatmentplans to a user; and allow the user to select from the displayedtreatment plans the treatment plan that is appropriate for a currenttreatment session.